Learning Statistics with R

Back in the grimdark pre-Snapchat era of humanity (i.e. early 2011), I started teaching an introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. I wrote my own lecture notes for the class, which have now expanded to the point of effectively being a book.

The book is freely available, and as of the current update (version 0.6) it is released under a creative commons licence (CC BY-SA 4.0)

The book is associated with the lsr package, available on CRAN and github. The package is probably okay for many introductory teaching purposes, but I should note that this package isn't being actively updated, and I would strongly recommend taking care if you are thinking of using it in real world data analysis.

The decision to release the book under a Creative Commons licence reflects the fact that I've found very little time to update the book myself, and I feel a little bad about that. There have been a few people who have asked about adapting the book or using it in teaching, and I've invariably said "yes, please do!" so it seems like a sensible thing to do is throw it open for anyone to adapt it if they would like to. Like most people writing open textbooks, I chose a BY-SA licence. As per the licence "This means that this book can be reused, remixed, retained, revised and redistributed (including commercially) as long as appropriate credit is given to the authors. If you remix, or modify the original version of this open textbook, you must redistribute all versions of this open textbook under the same license - CC BY-SA."

There are a few projects that I'm aware of that adapt or extend LSR in different directions:

Table of Contents

I. Background

Chapter 1: Why do we learn statistics? Psychology and statistics. Statistics in everyday life. Some examples where intuition is misleading, and statistics is critical.

Chapter 2: A brief introduction to research design. Basics of psychological measurement. Reliability and validity of a measurement. Experimental and non-experimental design. Predictors versus outcomes.

VI. Other topics

Chapter 18: Epilogue. Comments on the content missing from this book. Advantages to using R.

References. Massively incomplete reference list.

Additional resources

Strictly speaking the book isn't linked to any particular lecture slides or exercises. However, I have on occasions run some brief one-day workshops at a few places, and it tends to loosely follow the book. The workshop consists of three parts, an introduction to the basic mechanics of R, followed by a fairly rapid overview of some core statistical tools in R, and some extra bits and pieces at the end: